• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.203-211
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• 1999

수리 가능한 시스템의 평균고장간격시간에 대한 많은 연구들이 진행되어 왔으며, 그 대부분은 n번째 고장발생시각 $T_n$을 관측한 후 그 다음 고장이 발생할 때까지의 평균시간, 즉 E($T_{n+1}$-$T_n$$\mid$$T_n$ = $t_n$)에 관한 연구들이었다. 본 연구에서는 수리가능한 시스템의 고장이 와이블과정을 따라 일어날 경우, n번째와 n+1번째 고장간의 평균고장간격시간 E($T_{n+1}$-$T_n$)에 대한 불편추정량을 구하고 일치성 및 근사적 정규성을 증명하였다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.1
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• pp.79-84
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• 2003

종래의 연구들은 주로 시간이 경과함에 따라 수리비용과 고장시간 간격이 고정된 상태에서 최적교환시각(T)을 구하는 조건을 발견하는데 중점을 두었으나, 대부분의 시스템은 시간이 경과할수록 고장시간간격이 좁아지고 수리비용은 증가하는 것이 일반적이다. 본 논문에서는 위의 두 조건을 만족하는 보다 현실적인 모델을 구축하였으며, 또 일정시간 내에 2개의 시스템이 존재할 때 어느 조건 하에서 시스템이 확률적으로 우월한가를 분석하는 연구를 수행하였다. 즉, 시스템은 시간이 경과함에 따라 확률 P［N=k］로서 완전수리를, 1-P［N=k］로서 소수리를 행하는 모델을 고려하였다. 여기서 N은 연속된 완전수리 사이의 소수리의 수를 나타낸다. 또한 초기고장에 있어서 수리에 의해 새로운 시스템이 되는 확률이 높고, 고장횟수가 증가함에 따라 완전수리가 행해지는 확률이 낮아지는, 보다 현실에 가까운 모델을 구축하였다. 모델을 일반화하기 위해 수리비용은 확률변수로 가정하였다.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.387-392
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• 2001

This Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling for grouped failure data that is able to predict cumulative failures in the variable future time. The two variant models of cascade-correlation learning (CasCor) algorithm are presented. Suggested models are compared with other well-known NN models and statistical software reliability growth models (SRGMs). Experimental results show that the suggested models show better predictability.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.216-224
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• 1999

In the paper we propose two recovery methods by adopting a rollback and/or roll-forward technique (S) to recover TMR failures in a TMR (structured ) system that is the simplest spatial redundancy. This technique is apparently effective to recovering TMR failures primarily caused by transient faults. The proposed policies carry out few reconfigurations at the cost of (minimal) time-overhead needed for those time-redundant schemes. The optimal checkpoint-interval vectors are derived for both methods through the likelihoods of all (possible) states of the system as well as the total execution-time. Consequently the effectiveness of our proposed policies is validated through certain numerical examples and simulations.

• Convergence Security Journal
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• v.7 no.2
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• pp.49-55
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• 2007

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. From the subdivision of this analysis, new attemp needs the side of the quality control. In this paper, we discuss process capability analysis using process capability indexs. Because of software failure interval time is pattern of nonnegative value, instead of capability analysis of suppose to normal distribution, capability analysis of process distribution using to Box-Cox transformation is attermpted. The used software failure time data for capability analysis of process is SS3, the result of analysis listed on this chapter 4 and 5. The practical use is presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.781-787
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• 2010

Forecasting possible failure characteristics is very important in maintenance planning because it helps in predicting any future failures and determining the optimum replacement interval. This paper examines the time.to-failure distribution of the transfer gearbox of a J79 engine by using a probability plotting technique which is one of the most convenient techniques for reliability analysis. Various probability distributions are evaluated for determining the suitable probability distribution of the failure data of the transfer gearbox, and the resulting correlation coefficient indicates that failure data have a lognormal distribution. The expected number of unscheduled maintenance actions and the optimum replacement interval for various values of cost ratios are determined.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.53-64
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• 1998

Recently, it is of great interest among engineers and reliability scientists to consider a statistical model to describe the failure times of various types of repairable systems. The main subject we deal with in this paper is the power law process which is proved to be a useful model to describe the reliability growth of the repairable system. In particular, we derive the bootstrap confidence intervals of the mean time between two successive failures of a repairable system using the time truncated data. We also compare our bootstrap confindence intervals with Crow's (1982) confidence interval.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.205-218
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• 1996

본 연구는 써버의 고장을 허용하는 단수써버 Queueing 시스템의 확률적 모델을 제시한 것으로, 써버는 N 제어 정책에 의하여 작동되며, 도착은 Stationary compound poisson에 의하여 이루어지고, 서비스 시간에 대한 분포는 Erlang에 의하여 발생하며, 수리시간에 대한 분포는 평균이 일정한 분포에 의하여 생성되는 경우를 고려하였다. 또한 고장간격 시간은 일정한 평균을 가진 임의의 분포를 가진 Renewal process에 의한다고 가정하였고, 완료 시간의 개념은 재생과정의 적용방법에 의하여 유도할 수 있으며, 시스템 크기의 확율 생성 함수의 값이 구해진다는 것을 제시하였다.

• Convergence Security Journal
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• v.10 no.1
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• pp.55-61
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• 2010

It is of great practical interest to deciding when to stop testing a software system in development phase and transfer it to the user. This decision problem called an optimal release policies. In this paper, because of the possibility of introducing new faults when correcting or modifying the software, we were researched release comparative policies which based on infinite failure NHPP model and types of interval failure times. The policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement can optimal software release times. In a numerical example, applied data which were patterns, if intensity function constant or increasing, decreasing, estimated software optimal release time.